In the following paragraphs, when a reference “A” designates a signal A, “A(n)” designates the digitized value of the signal A at a discrete time n.
The input signal Sin may represent any type of digital video, audio or communication data. The signal Sin represents, for example, video or audio data. The input signal Sin may be, for example, an intermediate frequency modulated by the compressed video data originated from a base station. It is transmitted from a base station to a receiver over terrestrial broadcast, cable or satellite channel. During transmission, the signal Sin may have been subjected to various forms of distortions. The transmitted input signal Sin may comprise a training sequence. The received training sequence may be corrupted. In order to compensate for certain types of distortions, the possibly corrupted input signal Sin is filtered using an adaptive filter F. The filter F is adapted so that the received training sequence after filtering approximates the original training sequence that is known in advance by the receiver. The filter F has a set C of m adjustable coefficients having values C0(n), C1(n), . . ., Cm-1(n) at discrete time n. The filter F creates an output signal Sout and the value Sout(n) is obtained from the values of the filter coefficients at time n and values. of the input signal Sin buffered in the filter F at time n as shown in the equation of FIG. 1A.
To adapt the filter F, an error signal E is derived as a difference between a reference signal Sref and the signal Sout as shown in Equation of FIG. 1B. The reference signal is the training sequence known in advance by the receiver. This method of adaptive filtering or equalization relies on the training sequence to adapt the coefficients, and is often called a “trained equalization”.
An adaptation of the coefficients may also be performed by a so-called blind equalization. A blind equalization does not require transmission of a training sequence. The derivation of the error signal E and the adaptation of the coefficients rely on statistical properties of the adaptive filter's output signal Sout.
A possible adaptation method consists of updating the set C so as to minimize the error signal E. Updating the set C is usually carried out by a method which updates each coefficient as the time increments from time n to time n+1. A form of an update equation for a coefficient Cj at time n is given by FIG. 1C where g is a mathematical scalar function of the time n, f1 is a mathematical scalar function of the value of the error signal at time n, and f2 is a mathematical scalar function of the value of the input signal Sin(n-j that is, at time n, buffered in the filter F and associated with the coefficient Cj
A well-known adaptation equation having a form shown in FIG. 1D relates to the Least Mean Square (LMS) algorithm, which is intended to adjust a coefficient of the filter F so as to minimize a discrepancy between the output signal of the filter F and a reference signal. The coefficient Cj(n) is updated using an adaptation step parameter μ that controls the rate of convergence of the algorithm.
The U.S. Pat. No. 5,568,411, herein incorporated by reference, discloses a method of updating the coefficients of an adaptive filter. The method closely resembles the well-known LMS update algorithm. The known method addresses a coefficient update relationship based on a polarity-coincidence correlator that is based on a sum of products of the sign of the error signal and the signs of consecutive N time samples of the input signal.